Overview

Dataset statistics

Number of variables12
Number of observations320
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)2.5%
Total size in memory30.1 KiB
Average record size in memory96.4 B

Variable types

Numeric12

Alerts

Dataset has 8 (2.5%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
pH is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
citric acid has 27 (8.4%) zerosZeros

Reproduction

Analysis started2023-04-16 15:51:28.700746
Analysis finished2023-04-16 15:52:09.542086
Duration40.84 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct76
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3034375
Minimum5
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:09.642010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.095
Q17.075
median7.9
Q39.225
95-th percentile11.9
Maximum15.5
Range10.5
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.8095759
Coefficient of variation (CV)0.21793094
Kurtosis1.2660513
Mean8.3034375
Median Absolute Deviation (MAD)1.05
Skewness1.0509096
Sum2657.1
Variance3.2745649
MonotonicityNot monotonic
2023-04-16T11:52:09.981272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 19
 
5.9%
6.8 12
 
3.8%
7 11
 
3.4%
8.3 11
 
3.4%
6.7 10
 
3.1%
7.9 10
 
3.1%
7.3 9
 
2.8%
7.6 9
 
2.8%
8.2 9
 
2.8%
7.1 9
 
2.8%
Other values (66) 211
65.9%
ValueCountFrequency (%)
5 2
 
0.6%
5.1 1
 
0.3%
5.4 2
 
0.6%
5.5 1
 
0.3%
5.6 2
 
0.6%
5.7 1
 
0.3%
5.8 2
 
0.6%
5.9 3
0.9%
6 2
 
0.6%
6.1 7
2.2%
ValueCountFrequency (%)
15.5 1
0.3%
15 1
0.3%
14.3 1
0.3%
14 1
0.3%
13.5 1
0.3%
12.9 1
0.3%
12.8 1
0.3%
12.7 2
0.6%
12.4 1
0.3%
12.3 1
0.3%

volatile acidity
Real number (ℝ)

Distinct98
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.516875
Minimum0.12
Maximum1.115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:10.651303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.26
Q10.37
median0.52
Q30.63625
95-th percentile0.811
Maximum1.115
Range0.995
Interquartile range (IQR)0.26625

Descriptive statistics

Standard deviation0.17805396
Coefficient of variation (CV)0.34448167
Kurtosis0.0027277643
Mean0.516875
Median Absolute Deviation (MAD)0.13
Skewness0.37628574
Sum165.4
Variance0.031703213
MonotonicityNot monotonic
2023-04-16T11:52:10.942016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 14
 
4.4%
0.42 9
 
2.8%
0.36 9
 
2.8%
0.5 9
 
2.8%
0.63 8
 
2.5%
0.38 8
 
2.5%
0.31 8
 
2.5%
0.56 7
 
2.2%
0.49 7
 
2.2%
0.4 7
 
2.2%
Other values (88) 234
73.1%
ValueCountFrequency (%)
0.12 1
 
0.3%
0.18 2
 
0.6%
0.19 1
 
0.3%
0.21 1
 
0.3%
0.22 2
 
0.6%
0.23 1
 
0.3%
0.24 6
1.9%
0.25 1
 
0.3%
0.26 7
2.2%
0.27 4
1.2%
ValueCountFrequency (%)
1.115 1
0.3%
1.09 1
0.3%
1.01 1
0.3%
1 1
0.3%
0.975 1
0.3%
0.96 1
0.3%
0.91 2
0.6%
0.885 1
0.3%
0.88 1
0.3%
0.87 1
0.3%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.265
Minimum0
Maximum0.79
Zeros27
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:11.250268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.25
Q30.41
95-th percentile0.6005
Maximum0.79
Range0.79
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.19238154
Coefficient of variation (CV)0.72596809
Kurtosis-0.82045954
Mean0.265
Median Absolute Deviation (MAD)0.16
Skewness0.33582268
Sum84.8
Variance0.037010658
MonotonicityNot monotonic
2023-04-16T11:52:11.477169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
8.4%
0.49 13
 
4.1%
0.24 13
 
4.1%
0.08 11
 
3.4%
0.02 11
 
3.4%
0.39 10
 
3.1%
0.42 9
 
2.8%
0.04 8
 
2.5%
0.06 8
 
2.5%
0.34 7
 
2.2%
Other values (60) 203
63.4%
ValueCountFrequency (%)
0 27
8.4%
0.01 3
 
0.9%
0.02 11
3.4%
0.03 4
 
1.2%
0.04 8
 
2.5%
0.05 5
 
1.6%
0.06 8
 
2.5%
0.07 4
 
1.2%
0.08 11
3.4%
0.09 5
 
1.6%
ValueCountFrequency (%)
0.79 1
 
0.3%
0.74 1
 
0.3%
0.69 2
0.6%
0.68 2
0.6%
0.66 2
0.6%
0.65 2
0.6%
0.64 3
0.9%
0.63 2
0.6%
0.61 1
 
0.3%
0.6 1
 
0.3%

residual sugar
Real number (ℝ)

Distinct47
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4721875
Minimum1.2
Maximum13.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:11.685124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.6
Q11.9
median2.2
Q32.5625
95-th percentile4.3175
Maximum13.4
Range12.2
Interquartile range (IQR)0.6625

Descriptive statistics

Standard deviation1.3014309
Coefficient of variation (CV)0.52642889
Kurtosis25.229217
Mean2.4721875
Median Absolute Deviation (MAD)0.3
Skewness4.3627844
Sum791.1
Variance1.6937225
MonotonicityNot monotonic
2023-04-16T11:52:12.134069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2 37
 
11.6%
1.8 29
 
9.1%
1.9 26
 
8.1%
2.3 23
 
7.2%
2.2 23
 
7.2%
2.1 23
 
7.2%
2.5 16
 
5.0%
2.4 16
 
5.0%
1.7 15
 
4.7%
2.6 15
 
4.7%
Other values (37) 97
30.3%
ValueCountFrequency (%)
1.2 3
 
0.9%
1.4 8
 
2.5%
1.5 2
 
0.6%
1.6 15
4.7%
1.65 1
 
0.3%
1.7 15
4.7%
1.8 29
9.1%
1.9 26
8.1%
2 37
11.6%
2.1 23
7.2%
ValueCountFrequency (%)
13.4 1
0.3%
11 1
0.3%
8.6 1
0.3%
8.3 2
0.6%
7.3 1
0.3%
7 1
0.3%
6.6 1
0.3%
6.1 1
0.3%
6 1
0.3%
5.8 2
0.6%

chlorides
Real number (ℝ)

Distinct84
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08354375
Minimum0.039
Maximum0.422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:12.503177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.039
5-th percentile0.0539
Q10.068
median0.078
Q30.088
95-th percentile0.122
Maximum0.422
Range0.383
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.036431298
Coefficient of variation (CV)0.43607449
Kurtosis45.730076
Mean0.08354375
Median Absolute Deviation (MAD)0.01
Skewness5.707414
Sum26.734
Variance0.0013272395
MonotonicityNot monotonic
2023-04-16T11:52:12.860993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.078 14
 
4.4%
0.074 14
 
4.4%
0.084 12
 
3.8%
0.076 12
 
3.8%
0.071 11
 
3.4%
0.083 10
 
3.1%
0.066 10
 
3.1%
0.082 9
 
2.8%
0.077 9
 
2.8%
0.08 9
 
2.8%
Other values (74) 210
65.6%
ValueCountFrequency (%)
0.039 1
 
0.3%
0.041 2
0.6%
0.044 3
0.9%
0.046 1
 
0.3%
0.047 2
0.6%
0.048 2
0.6%
0.049 1
 
0.3%
0.05 2
0.6%
0.051 1
 
0.3%
0.052 1
 
0.3%
ValueCountFrequency (%)
0.422 1
0.3%
0.413 1
0.3%
0.263 1
0.3%
0.226 1
0.3%
0.19 1
0.3%
0.186 1
0.3%
0.17 1
0.3%
0.169 1
0.3%
0.166 1
0.3%
0.152 2
0.6%

free sulfur dioxide
Real number (ℝ)

Distinct45
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.870313
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:13.318121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median13
Q321.5
95-th percentile36
Maximum72
Range71
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation11.043639
Coefficient of variation (CV)0.69586779
Kurtosis2.6128544
Mean15.870313
Median Absolute Deviation (MAD)6
Skewness1.4198116
Sum5078.5
Variance121.96197
MonotonicityNot monotonic
2023-04-16T11:52:13.508470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6 26
 
8.1%
5 18
 
5.6%
8 18
 
5.6%
11 17
 
5.3%
10 17
 
5.3%
15 16
 
5.0%
9 15
 
4.7%
12 13
 
4.1%
16 13
 
4.1%
13 13
 
4.1%
Other values (35) 154
48.1%
ValueCountFrequency (%)
1 2
 
0.6%
3 8
 
2.5%
4 12
3.8%
5 18
5.6%
6 26
8.1%
7 11
3.4%
8 18
5.6%
9 15
4.7%
10 17
5.3%
11 17
5.3%
ValueCountFrequency (%)
72 1
 
0.3%
54 1
 
0.3%
52 1
 
0.3%
51 3
0.9%
48 1
 
0.3%
46 1
 
0.3%
45 1
 
0.3%
42 1
 
0.3%
39 1
 
0.3%
37.5 1
 
0.3%

total sulfur dioxide
Real number (ℝ)

Distinct101
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.710938
Minimum7
Maximum278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:13.922114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11
Q123
median37
Q360
95-th percentile103.15
Maximum278
Range271
Interquartile range (IQR)37

Descriptive statistics

Standard deviation32.748663
Coefficient of variation (CV)0.71642949
Kurtosis8.2718831
Mean45.710938
Median Absolute Deviation (MAD)18
Skewness2.0566399
Sum14627.5
Variance1072.475
MonotonicityNot monotonic
2023-04-16T11:52:14.361683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 12
 
3.8%
15 11
 
3.4%
27 10
 
3.1%
31 9
 
2.8%
24 9
 
2.8%
44 9
 
2.8%
11 8
 
2.5%
28 8
 
2.5%
58 7
 
2.2%
25 7
 
2.2%
Other values (91) 230
71.9%
ValueCountFrequency (%)
7 1
 
0.3%
8 2
 
0.6%
9 2
 
0.6%
10 4
 
1.2%
11 8
2.5%
12 7
2.2%
13 3
 
0.9%
14 4
 
1.2%
15 11
3.4%
16 3
 
0.9%
ValueCountFrequency (%)
278 1
0.3%
160 1
0.3%
153 1
0.3%
148 2
0.6%
147 1
0.3%
134 1
0.3%
131 1
0.3%
126 1
0.3%
121 1
0.3%
115 1
0.3%

density
Real number (ℝ)

Distinct178
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99663756
Minimum0.9908
Maximum1.00315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:14.654479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9908
5-th percentile0.993359
Q10.9954575
median0.9965
Q30.99768
95-th percentile1.0000025
Maximum1.00315
Range0.01235
Interquartile range (IQR)0.0022225

Descriptive statistics

Standard deviation0.0020072478
Coefficient of variation (CV)0.0020140198
Kurtosis0.46613778
Mean0.99663756
Median Absolute Deviation (MAD)0.0011
Skewness0.14443
Sum318.92402
Variance4.0290436 × 10-6
MonotonicityNot monotonic
2023-04-16T11:52:15.077192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9962 10
 
3.1%
0.9976 8
 
2.5%
0.9972 8
 
2.5%
0.9971 6
 
1.9%
0.9964 6
 
1.9%
0.9966 6
 
1.9%
0.998 6
 
1.9%
0.9974 6
 
1.9%
0.9958 6
 
1.9%
0.9986 5
 
1.6%
Other values (168) 253
79.1%
ValueCountFrequency (%)
0.9908 1
0.3%
0.99084 1
0.3%
0.99162 1
0.3%
0.9917 1
0.3%
0.99191 1
0.3%
0.99235 1
0.3%
0.9924 1
0.3%
0.99258 1
0.3%
0.99264 1
0.3%
0.99286 1
0.3%
ValueCountFrequency (%)
1.00315 1
 
0.3%
1.0022 1
 
0.3%
1.0018 1
 
0.3%
1.0015 1
 
0.3%
1.0014 3
0.9%
1.001 1
 
0.3%
1.0008 1
 
0.3%
1.0006 2
0.6%
1.0004 1
 
0.3%
1.0003 1
 
0.3%

pH
Real number (ℝ)

Distinct68
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3089687
Minimum2.86
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:15.219116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.04
Q13.21
median3.32
Q33.4
95-th percentile3.5505
Maximum4.01
Range1.15
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15608413
Coefficient of variation (CV)0.047170023
Kurtosis1.169744
Mean3.3089687
Median Absolute Deviation (MAD)0.1
Skewness0.17974961
Sum1058.87
Variance0.024362256
MonotonicityNot monotonic
2023-04-16T11:52:15.598514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.21 13
 
4.1%
3.36 13
 
4.1%
3.35 12
 
3.8%
3.34 12
 
3.8%
3.39 10
 
3.1%
3.32 10
 
3.1%
3.44 9
 
2.8%
3.4 9
 
2.8%
3.41 9
 
2.8%
3.26 8
 
2.5%
Other values (58) 215
67.2%
ValueCountFrequency (%)
2.86 1
 
0.3%
2.87 1
 
0.3%
2.89 1
 
0.3%
2.92 1
 
0.3%
2.98 2
0.6%
3 2
0.6%
3.01 2
0.6%
3.03 3
0.9%
3.04 4
1.2%
3.05 1
 
0.3%
ValueCountFrequency (%)
4.01 1
0.3%
3.72 2
0.6%
3.7 1
0.3%
3.67 2
0.6%
3.66 2
0.6%
3.61 1
0.3%
3.6 2
0.6%
3.59 2
0.6%
3.57 1
0.3%
3.56 2
0.6%

sulphates
Real number (ℝ)

Distinct67
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65065625
Minimum0.33
Maximum1.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:15.973356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.4595
Q10.55
median0.61
Q30.74
95-th percentile0.9205
Maximum1.36
Range1.03
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14739283
Coefficient of variation (CV)0.22652949
Kurtosis2.2919705
Mean0.65065625
Median Absolute Deviation (MAD)0.08
Skewness1.1915125
Sum208.21
Variance0.021724646
MonotonicityNot monotonic
2023-04-16T11:52:16.110411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 18
 
5.6%
0.59 16
 
5.0%
0.58 15
 
4.7%
0.56 14
 
4.4%
0.53 14
 
4.4%
0.6 13
 
4.1%
0.52 13
 
4.1%
0.57 12
 
3.8%
0.66 12
 
3.8%
0.63 10
 
3.1%
Other values (57) 183
57.2%
ValueCountFrequency (%)
0.33 1
 
0.3%
0.37 1
 
0.3%
0.39 2
 
0.6%
0.4 1
 
0.3%
0.42 1
 
0.3%
0.43 1
 
0.3%
0.44 3
0.9%
0.45 6
1.9%
0.46 1
 
0.3%
0.47 3
0.9%
ValueCountFrequency (%)
1.36 1
0.3%
1.2 1
0.3%
1.17 1
0.3%
1.09 1
0.3%
1.07 1
0.3%
1.06 2
0.6%
1.05 1
0.3%
1.02 1
0.3%
0.98 1
0.3%
0.97 2
0.6%

alcohol
Real number (ℝ)

Distinct48
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4425
Minimum8.4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:16.269779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.1
Q311.2
95-th percentile12.505
Maximum14
Range5.6
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.1175109
Coefficient of variation (CV)0.10701565
Kurtosis0.04719769
Mean10.4425
Median Absolute Deviation (MAD)0.7
Skewness0.83976993
Sum3341.6
Variance1.2488307
MonotonicityNot monotonic
2023-04-16T11:52:16.532334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
9.5 27
 
8.4%
9.4 22
 
6.9%
9.2 16
 
5.0%
10 15
 
4.7%
9.8 14
 
4.4%
10.1 13
 
4.1%
9.3 12
 
3.8%
11 12
 
3.8%
11.8 11
 
3.4%
9.6 11
 
3.4%
Other values (38) 167
52.2%
ValueCountFrequency (%)
8.4 1
 
0.3%
8.5 1
 
0.3%
8.7 2
 
0.6%
9 5
 
1.6%
9.1 3
 
0.9%
9.2 16
5.0%
9.3 12
3.8%
9.4 22
6.9%
9.5 27
8.4%
9.6 11
3.4%
ValueCountFrequency (%)
14 3
0.9%
13.2 1
 
0.3%
13.1 1
 
0.3%
13 3
0.9%
12.9 3
0.9%
12.8 3
0.9%
12.6 2
 
0.6%
12.5 6
1.9%
12.4 1
 
0.3%
12.3 5
1.6%

quality
Real number (ℝ)

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.684375
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-04-16T11:52:16.661948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80966318
Coefficient of variation (CV)0.14243662
Kurtosis0.18137915
Mean5.684375
Median Absolute Deviation (MAD)1
Skewness0.31542728
Sum1819
Variance0.65555447
MonotonicityNot monotonic
2023-04-16T11:52:16.843243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 132
41.2%
5 130
40.6%
7 42
 
13.1%
4 10
 
3.1%
8 5
 
1.6%
3 1
 
0.3%
ValueCountFrequency (%)
3 1
 
0.3%
4 10
 
3.1%
5 130
40.6%
6 132
41.2%
7 42
 
13.1%
8 5
 
1.6%
ValueCountFrequency (%)
8 5
 
1.6%
7 42
 
13.1%
6 132
41.2%
5 130
40.6%
4 10
 
3.1%
3 1
 
0.3%

Interactions

2023-04-16T11:52:05.424219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:29.147490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:32.505288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:36.184401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:39.772113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:43.594316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:46.887101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:50.101619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:53.744246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:56.516219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:59.223205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:02.039148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:05.773381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:29.244666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:32.773333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:36.572028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:40.059678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:44.135433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:47.294208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:50.572628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:54.058127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:56.671722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:59.389130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:02.355163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:06.145216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:29.476186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:33.127709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:36.681324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:40.392452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:44.508240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:47.584110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:50.915162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:54.384464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:57.010584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:59.550086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:02.726262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:06.440106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:29.731268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:33.526781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:37.062616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:40.791977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:44.940334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:47.911357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:51.320340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:54.694222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:57.347462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:59.912030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:03.058076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:06.645217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:30.125208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:33.728540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:37.459462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:41.127101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:45.155704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:48.035092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:51.617674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:54.891907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:57.529169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:00.141542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:03.364160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:06.984147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:30.374199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:34.039573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:37.881635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:41.487062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:45.295093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:48.442452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:51.999271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:55.108668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:57.935125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:00.270504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:03.566346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:07.351349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:30.707934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:34.400091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:38.047486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:41.741535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:45.601668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:48.853571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:52.388709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:55.214033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.114299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:00.485484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:03.785690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:07.599170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:30.851433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:34.725160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:38.251283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:42.110436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:45.809193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:48.973965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:52.554379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:55.317975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.279365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:00.730252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:03.965066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:07.836614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:31.149232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:35.038391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:38.564225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:42.285057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:45.915155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:49.277422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:52.887094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:55.531266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.520205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:01.102115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:04.146472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:07.966025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:31.500241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:35.234687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:38.787162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:42.664171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:46.089454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:49.447048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:53.088758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:55.871614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.703400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:01.416374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:04.431287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:08.191164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:31.830491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:35.562701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:38.973247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:42.950925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:46.467189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:49.706458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:53.223766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:56.141766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.867767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:01.611519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:04.756069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:08.445172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:32.199416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:35.901743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:39.386358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:43.214326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:46.727092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:49.860318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:53.365234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:56.425187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:51:58.976559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:01.766955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-16T11:52:05.124118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-16T11:52:16.998762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.3440.6690.2830.249-0.180-0.0850.644-0.7170.242-0.0620.166
volatile acidity-0.3441.000-0.6360.0100.109-0.0210.0520.0080.294-0.259-0.193-0.427
citric acid0.669-0.6361.0000.1840.060-0.0620.0050.317-0.5590.3170.1140.267
residual sugar0.2830.0100.1841.0000.3040.1500.2290.392-0.1950.0550.0600.030
chlorides0.2490.1090.0600.3041.0000.0190.1720.462-0.2030.085-0.306-0.218
free sulfur dioxide-0.180-0.021-0.0620.1500.0191.0000.760-0.0730.1100.019-0.040-0.048
total sulfur dioxide-0.0850.0520.0050.2290.1720.7601.0000.1240.008-0.004-0.210-0.172
density0.6440.0080.3170.3920.462-0.0730.1241.000-0.3340.211-0.529-0.186
pH-0.7170.294-0.559-0.195-0.2030.1100.008-0.3341.000-0.0770.128-0.106
sulphates0.242-0.2590.3170.0550.0850.019-0.0040.211-0.0771.0000.1340.344
alcohol-0.062-0.1930.1140.060-0.306-0.040-0.210-0.5290.1280.1341.0000.472
quality0.166-0.4270.2670.030-0.218-0.048-0.172-0.186-0.1060.3440.4721.000

Missing values

2023-04-16T11:52:08.750294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T11:52:09.303150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.70.560.082.500.11414.046.00.997103.240.669.66
17.80.500.171.600.08221.0102.00.996003.390.489.55
210.70.670.222.700.10717.034.01.000403.280.989.96
38.50.460.312.250.07832.058.00.998003.330.549.85
46.70.460.241.700.07718.034.00.994803.390.6010.66
57.20.410.302.100.08335.072.00.997003.440.529.45
67.70.540.261.900.08923.0147.00.996363.260.599.75
77.00.780.082.000.09310.019.00.995603.400.4710.05
88.20.390.381.500.05810.029.00.996203.260.749.85
95.80.610.111.800.06618.028.00.994833.550.6610.96
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3107.60.5400.021.70.08517.031.00.995893.370.5110.46
3118.00.6200.332.70.08816.037.00.997203.310.5810.76
3127.00.3600.212.40.08624.069.00.995563.400.5310.16
31311.90.5800.581.90.0715.018.00.998003.090.6310.06
31410.20.6700.391.90.0546.017.00.997603.170.4710.05
3156.80.6400.002.70.12315.033.00.995383.440.6311.36
3166.60.6300.004.30.09351.077.50.995583.200.459.55
3178.30.6000.252.20.1189.038.00.996163.150.539.85
3188.80.2700.392.00.10020.027.00.995463.150.6911.26
3199.10.7650.041.60.0784.014.00.998003.290.549.74

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
17.20.3600.462.10.07424.044.00.995343.400.8511.073
38.30.2600.422.00.08011.027.00.997403.210.809.463
06.80.6500.022.10.0788.015.00.994983.350.6210.462
27.90.5450.064.00.08727.061.00.996503.360.6710.762
49.10.2200.242.10.0781.028.00.999003.410.8710.362
59.90.3500.412.30.08311.061.00.998203.210.509.552
610.00.5600.242.20.07919.058.00.999103.180.5610.162
710.20.6700.391.90.0546.017.00.997603.170.4710.052